A combination of wearable Respiratory Inductive Plethysmograph and a hand-to-mouth Proximity Sensor (PS) can be used to monitor smoking habits and smoke exposure in cigarette smokers. In our previous work, detection of smoke inhalations was achieved by using a Support Vector Machine (SVM) classifier applied to raw sensor signals with 1503-element feature vectors. This study uses empirically-defined 27 features computed from the sensor signals to reduce the length of vectors. Further reduction in the length of the feature vectors was achieved by a forward feature selection algorithm, identifying from 2 to 16 features most critical for smoke inhalations detection. For individual detection models, the 1503-element feature vectors, 27-element feature vectors and reduced feature vectors resulted in F-scores of 90.1%, 68.7% and 94% respectively. For the group models, F-scores were 81.3%, 65% and 67% respectively. These results demonstrate feasibility of detecting smoke inhalations with a computed feature set, but suggest high individuality of breathing patterns associated with smoking.